community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)

Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes

libs/community/langchain_community/llms/nlpcloud.py

@@ -0,0 +1,145 @@
+from typing import Any, Dict, List, Mapping, Optional
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.pydantic_v1 import Extra, SecretStr, root_validator
+from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+
+
+class NLPCloud(LLM):
+    """NLPCloud large language models.
+
+    To use, you should have the ``nlpcloud`` python package installed, and the
+    environment variable ``NLPCLOUD_API_KEY`` set with your API key.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import NLPCloud
+            nlpcloud = NLPCloud(model="finetuned-gpt-neox-20b")
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = "finetuned-gpt-neox-20b"
+    """Model name to use."""
+    gpu: bool = True
+    """Whether to use a GPU or not"""
+    lang: str = "en"
+    """Language to use (multilingual addon)"""
+    temperature: float = 0.7
+    """What sampling temperature to use."""
+    max_length: int = 256
+    """The maximum number of tokens to generate in the completion."""
+    length_no_input: bool = True
+    """Whether min_length and max_length should include the length of the input."""
+    remove_input: bool = True
+    """Remove input text from API response"""
+    remove_end_sequence: bool = True
+    """Whether or not to remove the end sequence token."""
+    bad_words: List[str] = []
+    """List of tokens not allowed to be generated."""
+    top_p: int = 1
+    """Total probability mass of tokens to consider at each step."""
+    top_k: int = 50
+    """The number of highest probability tokens to keep for top-k filtering."""
+    repetition_penalty: float = 1.0
+    """Penalizes repeated tokens. 1.0 means no penalty."""
+    num_beams: int = 1
+    """Number of beams for beam search."""
+    num_return_sequences: int = 1
+    """How many completions to generate for each prompt."""
+
+    nlpcloud_api_key: Optional[SecretStr] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        values["nlpcloud_api_key"] = convert_to_secret_str(
+            get_from_dict_or_env(values, "nlpcloud_api_key", "NLPCLOUD_API_KEY")
+        )
+        try:
+            import nlpcloud
+
+            values["client"] = nlpcloud.Client(
+                values["model_name"],
+                values["nlpcloud_api_key"].get_secret_value(),
+                gpu=values["gpu"],
+                lang=values["lang"],
+            )
+        except ImportError:
+            raise ImportError(
+                "Could not import nlpcloud python package. "
+                "Please install it with `pip install nlpcloud`."
+            )
+        return values
+
+    @property
+    def _default_params(self) -> Mapping[str, Any]:
+        """Get the default parameters for calling NLPCloud API."""
+        return {
+            "temperature": self.temperature,
+            "max_length": self.max_length,
+            "length_no_input": self.length_no_input,
+            "remove_input": self.remove_input,
+            "remove_end_sequence": self.remove_end_sequence,
+            "bad_words": self.bad_words,
+            "top_p": self.top_p,
+            "top_k": self.top_k,
+            "repetition_penalty": self.repetition_penalty,
+            "num_beams": self.num_beams,
+            "num_return_sequences": self.num_return_sequences,
+        }
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            **{"model_name": self.model_name},
+            **{"gpu": self.gpu},
+            **{"lang": self.lang},
+            **self._default_params,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "nlpcloud"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out to NLPCloud's create endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Not supported by this interface (pass in init method)
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = nlpcloud("Tell me a joke.")
+        """
+        if stop and len(stop) > 1:
+            raise ValueError(
+                "NLPCloud only supports a single stop sequence per generation."
+                "Pass in a list of length 1."
+            )
+        elif stop and len(stop) == 1:
+            end_sequence = stop[0]
+        else:
+            end_sequence = None
+        params = {**self._default_params, **kwargs}
+        response = self.client.generation(prompt, end_sequence=end_sequence, **params)
+        return response["generated_text"]